Gautam Pant

Gautam Pant

Professor of Business Administration

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Contact

4037 Business Instructional Facility

515 Gregory Dr

Champaign, IL 61820

217-333-7907

gpant@illinois.edu

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Listings

Educational Background

  • Ph.D., Business Administration, University of Iowa, 2004
  • M.S., Computer Science, Baylor University, 1999
  • B.E., Computer Engineering, University of Mumbai, 1996

Positions Held

  • Professor, Business Administration, University of Illinois at Urbana-Champaign, 2022 to present
  • Professor, University of Iowa, 2021-2022
  • Associate Professor, University of Iowa, 2013-2021

Recent Publications

  • Yoon, J., Pant, G., & Pant, S. Forthcoming. Discovery of Technological Innovation Systems: Implications for Predicting Future Innovation Journal of Management Information Systems.
  • Pant, G., Yu, W., Ma, Z., & Hu, J. (2021). The effect of virtual tours on house price and time on market. Journal of Real Estate Literature, 28 (2), 133-149.

Other Publications

Articles

  • Pant, G., Liu, Y., & Sheng, O. (2020). Predicting labor market competition: Leveraging interfirm network and employee skills. Information Systems Research, 31 (4), 1443-1466.
  • Pant, G., Ketter, W., Padmanabhan, B., & Raghu, T. (2020). Addressing societal challenges through analytics: An ESG ice framework and research agenda. Journal of the Association for Information Systems, 21 (5), 1115-1127.
  • Pant, G., & Kim, I. (2019). Predicting web site audience demographics using content and design cues. Information and Management, 56 (5), 718-730.
  • Pant, G., & Pant, S. (2018). Visibility of Corporate Web Sites: The Role of Information Prosociality Decision Support Systems.
  • Pant, G., & Sheng, O. (2015). Web Footprints of Firms: Using Online Isomorphism for Competitor Identification. Information Systems Research, 26 (1), 188-209.
  • Pant, G., & Srinivasan, P. (2013). Status Locality on the Web: Implications for Building Focused Collections. Information Systems Research, 24 (3).
  • Pant, G., & Srinivasan, P. (2010). Predicting Web Page Status. Information Systems Research, 21 (2), 345-364.

Presentations

  • Yoon, J., Pant, G., & Pant, S. (2022). Discovery of Technological Innovation Systems: Implications for Predicting Future Innovation. INFORMS Workshop on Data Science.
  • Yoon, J., Pant, G., & Pant, S. (2021). Predicting Innovation Through Topical Structure Discovery. Workshop on Information Technology and Systems 2021.
  • Liu, Y., Pant, G., & Pant, S. (2021). Digital Prophylaxis for Firm Resilience: A Study On COVID-19 Disruption. Conference on Information Systems and Technology.
  • Pant, G., Liu, Y., & Sheng, O. (2020). Predicting Employee Turnover through Network Embeddedness. Workshop on Information Technology and Systems (WITS.
  • Ramaraju, N., Pant, G., & Pant, S. (2020). What's Good for the Goose is Good for the Gander: Gender Diversity of Inventors and Firm Innovation. Workshop on Information Systems and Economics (WISE.
  • Ding, Y., Zhou, X., & Pant, G. (2019). Deep Learning with Interaction Terms: An Experimental Exploration INFORMS Workshop on Data Science.

Honors and Awards

  • Best Student Paper Award, INFORMS Workshop on Data Science, 2022-2022
  • Best Paper Award Runner-up, 30th Workshop on Information Technology and Systems (WITS), 2020-2020

Service

  • Senior Editor, Information Systems Research, 2022 to present
  • Review Editor, Journal of the Association for Information Science and Technology (JASIST), 2017 to present
  • Co-Chair, 33rd Annual Workshop on Information Technologies and Systems (WITS’23), 2023-2023
  • Associate Editor, Service Science (INFORMS), 2020-2023
  • Co-Chair, INFORMS Workshop on Data Science, 2021-2021
  • Guest Co-Editor, Journal of the Association of Information Systems (JAIS) - Special Issue, 2017-2020

Current Courses

  • Data Science and Analytics (BADM 356) In this course, you will learn not only data analytic techniques but also the managerial implications of competing with analytics. You will understand the managerial challenges of using data analytics to develop a strategic advantage through readings and case studies. You will learn techniques such as statistical inference, linear modeling, sentiment analytics, and data mining through hands-on exercises in R. R is an open source language that has grown in importance and usage in corporations. Finally, you will be able to present and interpret data through an understanding of data visualization techniques.

  • Machine Learning in Bus Res (BADM 590) Special topics in the general area of business. Topics are selected by the instructor at the beginning of each term.

Contact

4037 Business Instructional Facility

515 Gregory Dr

Champaign, IL 61820

217-333-7907

gpant@illinois.edu

Google Scholar

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